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Prediction of placenta accreta spectrum using texture analysis on coronal and sagittal T2-weighted imaging
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Prediction of placenta accreta spectrum using texture analysis on coronal and sagittal T2-weighted imaging
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Prediction of placenta accreta spectrum using texture analysis on coronal and sagittal T2-weighted imaging
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Journal Article
Prediction of placenta accreta spectrum using texture analysis on coronal and sagittal T2-weighted imaging
2021
Overview
Purpose
To separately perform visual and texture analyses of the axial, coronal, and sagittal planes of T2-weighted images and identify the optimal method for differentiating between the normal placenta and placenta accreta spectrum (PAS).
Methods
Eighty consecutive patients (normal group,
n
= 50; PAS group,
n
= 30) underwent preoperative MRI. A scoring system (0–2) was used to evaluate the degree of abnormality observed in visual analysis (bulging, abnormal vascularity, T2 dark band, placental heterogeneity). The axial, coronal, and sagittal planes were manually segmented separately to obtain texture features, and seven combinations were obtained: axial; coronal; sagittal; axial and coronal; axial and sagittal; coronal and sagittal; and axial, coronal, and sagittal. Feature selection using the least absolute shrinkage and selection operator method and model construction using a support vector machine algorithm with k-fold cross-validation were performed. AUC was used to evaluate diagnostic performance.
Results
The AUC of visual analysis was 0.75. The model ‘coronal and sagittal’ had the highest AUC (0.98) amongst the seven combinations. The fivefold cross-validation for the model ‘coronal and sagittal’ showed AUCs of 0.85 and 0.97 in training and validation sets, respectively. The AUC of the model ‘coronal and sagittal’ for all subjects was significantly higher than that of visual analysis (0.98 vs. 0.75;
p
< 0.0001).
Conclusion
The model ‘coronal and sagittal’ can accurately differentiate between the normal placenta and PAS, with a significantly better diagnostic performance than visual analysis. Texture analysis is an optimal method for differentiating between the normal placenta and PAS.
